This paper offers a new hybrid probably asymptotically correct (PAC) reinforcement learning (RL) algorithm for Markov decision processes (MDPs) that intelligently maintains favorable features of its parents. The designed algorithm, referred to as the Dyna-Delayed Q-learning (DDQ) algorithm, combines model-free and model-based learning approaches while outperforming both in most cases. The paper includes a PAC analysis of the DDQ algorithm and a derivation of its sample complexity. Numerical results that support the claim regarding the new algorithm's sample efficiency compared to its parents are showcased in a small grid-world example.

The increased computerization in recent years has resulted in the production of a variety of different software, however measures need to be taken to ensure that the produced software isn't defective. Many researchers have worked in this area and have developed different Machine Learning-based approaches that predict whether the software is defective or not. This issue can't be resolved simply by using different conventional classifiers because the dataset is highly imbalanced i.e the number of defective samples detected is extremely less as compared to the number of non-defective samples. Therefore, to address this issue, certain sophisticated methods are required. The different methods developed by the researchers can be broadly classified into Resampling based methods, Cost-sensitive learning-based methods, and Ensemble Learning. Among these methods. This report analyses the performance of the Online Sequential Extreme Learning Machine (OS-ELM) proposed by Liang et.al. against several classifiers such as Logistic Regression, Support Vector Machine, Random Forest, and Na\"ive Bayes after oversampling the data. OS-ELM trains faster than conventional deep neural networks and it always converges to the globally optimal solution. A comparison is performed on the original dataset as well as the over-sampled data set. The oversampling technique used is Cluster-based Over-Sampling with Noise Filtering. This technique is better than several state-of-the-art techniques for oversampling. The analysis is carried out on 3 projects KC1, PC4 and PC3 carried out by the NASA group. The metrics used for measurement are recall and balanced accuracy. The results are higher for OS-ELM as compared to other classifiers in both scenarios.

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential but challenging problem. In particular, since the tensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task. Rather than exhaustively searching for the best tensor rank via trial-and-error experiments, Bayesian inference under the Gaussian-gamma prior was introduced in the context of probabilistic CPD modeling and it was shown to be an effective strategy for automatic tensor rank determination. This triggered flourishing research on other structured tensor CPDs with automatic tensor rank learning. As the other side of the coin, these research works also reveal that the Gaussian-gamma model does not perform well for high-rank tensors or/and low signal-to-noise ratios (SNRs). To overcome these drawbacks, in this paper, we introduce a more advanced generalized hyperbolic (GH) prior to the probabilistic CPD model, which not only includes the Gaussian-gamma model as a special case, but also provides more flexibilities to adapt to different levels of sparsity. Based on this novel probabilistic model, an algorithm is developed under the framework of variational inference, where each update is obtained in a closed-form. Extensive numerical results, using synthetic data and real-world datasets, demonstrate the excellent performance of the proposed method in learning both low as well as high tensor ranks even for low SNR cases.

Machine Learning Tutorial - Expectation-Maximization (EM) Algorithm In Machine Learning, covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model. You'll learn: What is EM Algorithm In Machine Learning? This Edureka video on'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.

Optimizing a partially observable Markov decision process (POMDP) policy is challenging. The policy graph improvement (PGI) algorithm for POMDPs represents the policy as a fixed size policy graph and improves the policy monotonically. Due to the fixed policy size, computation time for each improvement iteration is known in advance. Moreover, the method allows for compact understandable policies. This report describes the technical details of the PGI [1] and particle based PGI [2] algorithms for POMDPs in a more accessible way than [1] or [2] allowing practitioners and students to understand and implement the algorithms.

Grammar error handling (GEH) is an important topic in natural language processing (NLP). GEH includes both grammar error detection and grammar error correction. Recent advances in computation systems have promoted the use of deep learning (DL) models for NLP problems such as GEH. In this survey we focus on two main DL approaches for GEH: neural machine translation models and editor models. We describe the three main stages of the pipeline for these models: data preparation, training, and inference. Additionally, we discuss different techniques to improve the performance of these models at each stage of the pipeline. We compare the performance of different models and conclude with proposed future directions.

A surprising statistical phenomenon has emerged in modern machine learning: highly complex models can interpolate training data while still generalizing well to test data, even in the presence of label noise. This is rather striking as it the goes against the grain of the classical statistical wisdom which dictates that predictors that generalize well should trade off between the fit to the training data and the some measure of the complexity or smoothness of the predictor. Many estimators like neural networks, kernel estimators, nearest neighbour estimators, and even linear models have been shown to demonstrate this phenomenon (see, Zhang et al. 2017; Belkin et al. 2019, among others). This phenomenon has recently inspired intense theoretical research. One line of work (Soudry et al. 2018; Ji and Telgarsky 2019; Gunasekar et al. 2017; Nacson, Srebro, and Soudry 2019; Gunasekar et al. 2018a; Gunasekar et al. 2018b) formalized the argument (Neyshabur, Tomioka, and Srebro 2014; Neyshabur 2017) that, even when there is no explicit regularization that is used in training these rich models, there is nevertheless implicit regularization encoded in the choice of the optimization method used. For example, in the setting of linear classification, (Soudry et al. 2018; Ji and Telgarsky 2019; Nacson, Srebro, and Soudry 2019) show that learning a linear classifier using gradient descent on the unregularized logistic or exponential loss asymptotically leads the solution to converge to the maximum l

This article is part of a Machine Learning series. Our fourth expert is Dr. Eibe Frank, Associate Professor (Computer Science) at the University of Waikato, New Zealand. In this article, he talks about Weka and reveals what's under its hood. The idea behind Weka was to provide a uniform interface to a collection of machine learning algorithms in Java. This includes a graphical user interface, a command-line interface, and an API.

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field. Introduction RNAs are macromolecules of crucial and versatile biological functions. To fully understand their functions, knowledge of the three-dimensional (3D) structures is essential. Since experimental approaches to determinate RNA 3D structures are difficult and expensive, many computational approaches have been developed to this purpose. To date, although template-based and homology-modeling methods could achieve high accuracies, de novo predictions still depends on the size and complexity of the RNA, and further improvement in predicting non-canonical interactions are required, according to the recent RNA-Puzzles round III. For a comprehensive study of the recent work, we refer readers to the relevant literature.

A geo-marketplace allows users to be paid for their location data. Users concerned about privacy may want to charge more for data that pinpoints their location accurately, but may charge less for data that is more vague. A buyer would prefer to minimize data costs, but may have to spend more to get the necessary level of accuracy. We call this interplay between privacy, utility, and price \emph{spatial privacy pricing}. We formalize the issues mathematically with an example problem of a buyer deciding whether or not to open a restaurant by purchasing location data to determine if the potential number of customers is sufficient to open. The problem is expressed as a sequential decision making problem, where the buyer first makes a series of decisions about which data to buy and concludes with a decision about opening the restaurant or not. We present two algorithms to solve this problem, including experiments that show they perform better than baselines.